from fastapi import FastAPI
import urllib.parse

from config import __CONFIG__
import mysql_connector
from fastgpt_uploader import upload2fastgpt
from semanticscholar import search_paper
import rss 

app = FastAPI()

@app.get("/fastdoi")
async def get_reference(questions):
    print('Search: '+questions)
    questions = urllib.parse.quote(questions)
    res = []
    try:
        list = search_paper(query)
        for i in list:
            if mysql_connector.is_loaded(i['paperId']):
                print(i['paperId'])
            else:
                print(i['citationStyles']['bibtex'])
                res.append({
                    'id':i['paperId'],
                    'q':str(i['citationStyles']['bibtex']), 
                    'a':str(i['abstract']),
                    'score':[]
                    })
        print('New load: '+str(len(res))+'/'+str(len(list)))
    except Exception as e:
        print(str(e))
    if(upload2fastgpt(res)):
        for i in res:
            mysql_connector.new_load(i['id'])
    return res

@app.get("/rss")
async def load_rss():
    count = 0
    loaded = []
    uploads = []
    for entry in rss.load_content():
        loaded.append(entry['id'])
        uploads.append({
            'q': entry['title'],
            'a': entry['content']
        })
        count += 1
        if len(loaded) >= 100 and upload2fastgpt(uploads):
            rss.update(loaded)
            loaded = []
            uploads = []
    if len(loaded) > 0 and upload2fastgpt(uploads):
        rss.update(loaded)
    print(f"Total entries loaded: {count}")
    return f"Total entries loaded: {count}"

if __name__ == '__main__':
    import uvicorn
    uvicorn.run(app, host="127.0.0.1", port=8964)
    mysql_connector.end_mysql()